Characterizing tissue species using nuclear magnetic resonance (“NMR”) can include identifying different properties of a resonant species (e.g., T1 spin-lattice relaxation, T2 spin-spin relaxation, proton density). Other properties like tissue types and super-position of attributes can also be identified using NMR signals. These properties and others may be identified simultaneously using magnetic resonance fingerprinting (“MRF”), which is described, as one example, by D. Ma, et al., in “Magnetic Resonance Fingerprinting,” Nature, 2013; 495 (7440): 187-192.
Conventional magnetic resonance imaging (“MRI”) pulse sequences include repetitive similar preparation phases, waiting phases, and acquisition phases that serially produce signals from which images can be made. The preparation phase determines when a signal can be acquired and determines the properties of the acquired signal. For example, a first pulse sequence may produce a T1-weighted signal at a first echo time (“TE”), while a second pulse sequence may produce a T2-weighted signal at a second TE. These conventional pulse sequences typically provide qualitative results where data are acquired with various weightings or contrasts that highlight a particular parameter (e.g., T1 relaxation, T2 relaxation).
When magnetic resonance (“MR”) images are generated, they may be viewed by a radiologist and/or surgeon who interprets the qualitative images for specific disease signatures. The radiologist may examine multiple image types (e.g., T1-weighted, T2 -weighted) acquired in multiple imaging planes to make a diagnosis. The radiologist or other individual examining the qualitative images may need particular skill to be able to assess changes from session to session, from machine to machine, and from machine configuration to machine configuration.
Unlike conventional MRI, MRF employs a series of varied sequence blocks that simultaneously produce different signal evolutions in different resonant species (e.g., tissues) to which the radio frequency (“RF”) is applied. The signals from different resonant tissues will, however, be different and can be distinguished using MRF. The different signals can be collected over a period of time to identify a signal evolution for the volume. Resonant species in the volume can then be characterized by comparing the signal evolution to known evolutions. Characterizing the resonant species may include identifying a material or tissue type, or may include identifying MR parameters associated with the resonant species. The “known” evolutions may be, for example, simulated evolutions calculated from physical principles and/or previously acquired evolutions. A large set of known evolutions may be stored in a dictionary.
MRF provides an efficient way to simultaneously quantify multiple parameters, such as T1, T2, off-resonance, and proton density. The original implementation of MRF utilized a 2D slice-selective acquisition. A desirable step forward would be the ability to acquire multiple slices simultaneously in order to reduce scan time.